Comparison of in-distribution vs. out-distribution calibration.
Throughout the paper, we focused only on calibration and accuracy on common image corruptions. Regarding calibration on in-distribution images, it is known that \(\ell _p\)  adversarial training can degrade the calibration quality (e.g., see [1]}). However, with the small \(\operatorname{\mathbf {\varepsilon }}\)  that we use for adversarial training, this degradation is minimal. To illustrate this, we report below the calibration results on clean and corrupted CIFAR-10 data in Table REF .
We can see that, in line with the literature, \(\ell _p\)  adversarial training degrades the calibration on clean data but only slightly (from 2.9% to 4.0% for RLAT). At the same time, all adversarial training methods significantly improve the calibration on corrupted data (from 16.6% to 9.9% for RLAT).
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